Overview

Dataset statistics

Number of variables16
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory132.7 B

Variable types

Categorical2
Numeric14

Alerts

Casos_entre_nascidos is highly overall correlated with Espinha bífidaprop and 11 other fieldsHigh correlation
Espinha bífidaprop is highly overall correlated with Casos_entre_nascidos and 5 other fieldsHigh correlation
Outras malformações congênitas do sistema nervosoprop is highly overall correlated with Casos_entre_nascidos and 2 other fieldsHigh correlation
Malformações congênitas do aparelho circulatórioprop is highly overall correlated with Ausência atresia e estenose do intestino delgadoprop and 4 other fieldsHigh correlation
Fenda labial e fenda palatinaprop is highly overall correlated with Casos_entre_nascidos and 3 other fieldsHigh correlation
Ausência atresia e estenose do intestino delgadoprop is highly overall correlated with Malformações congênitas do aparelho circulatórioprop and 2 other fieldsHigh correlation
Outras malformações congênitas do aparelho digestivoprop is highly overall correlated with Casos_entre_nascidos and 6 other fieldsHigh correlation
Testiculo não-descidoprop is highly overall correlated with Casos_entre_nascidos and 8 other fieldsHigh correlation
Outras malformações do aparelho geniturinárioprop is highly overall correlated with Casos_entre_nascidos and 9 other fieldsHigh correlation
Deformidades congênitas do quadrilprop is highly overall correlated with Outras malformações congênitasprop and 2 other fieldsHigh correlation
Deformidades congênitas dos pésprop is highly overall correlated with Casos_entre_nascidos and 5 other fieldsHigh correlation
Outras malformações e deformidades congênitas do aparelho osteomuscularprop is highly overall correlated with Casos_entre_nascidos and 5 other fieldsHigh correlation
Outras malformações congênitasprop is highly overall correlated with Casos_entre_nascidos and 8 other fieldsHigh correlation
Anomalias cromossômicas não classificadas em outra parteprop is highly overall correlated with Casos_entre_nascidos and 6 other fieldsHigh correlation
UF is highly overall correlated with Casos_entre_nascidos and 14 other fieldsHigh correlation
Estado is highly overall correlated with Casos_entre_nascidos and 14 other fieldsHigh correlation
UF is uniformly distributedUniform
Estado is uniformly distributedUniform
UF has unique valuesUnique
Estado has unique valuesUnique
Casos_entre_nascidos has unique valuesUnique
Espinha bífidaprop has unique valuesUnique
Outras malformações congênitas do sistema nervosoprop has unique valuesUnique
Malformações congênitas do aparelho circulatórioprop has unique valuesUnique
Fenda labial e fenda palatinaprop has unique valuesUnique
Outras malformações congênitas do aparelho digestivoprop has unique valuesUnique
Testiculo não-descidoprop has unique valuesUnique
Outras malformações do aparelho geniturinárioprop has unique valuesUnique
Deformidades congênitas dos pésprop has unique valuesUnique
Outras malformações e deformidades congênitas do aparelho osteomuscularprop has unique valuesUnique
Outras malformações congênitasprop has unique valuesUnique
Anomalias cromossômicas não classificadas em outra parteprop has unique valuesUnique
Ausência atresia e estenose do intestino delgadoprop has 8 (29.6%) zerosZeros
Testiculo não-descidoprop has 1 (3.7%) zerosZeros
Deformidades congênitas do quadrilprop has 3 (11.1%) zerosZeros

Reproduction

Analysis started2023-04-16 17:08:32.794431
Analysis finished2023-04-16 17:08:52.151849
Duration19.36 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

UF
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
AC
 
1
PB
 
1
SP
 
1
SE
 
1
SC
 
1
Other values (22)
22 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters54
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowAC
2nd rowAL
3rd rowAM
4th rowAP
5th rowBA

Common Values

ValueCountFrequency (%)
AC 1
 
3.7%
PB 1
 
3.7%
SP 1
 
3.7%
SE 1
 
3.7%
SC 1
 
3.7%
RS 1
 
3.7%
RR 1
 
3.7%
RO 1
 
3.7%
RN 1
 
3.7%
RJ 1
 
3.7%
Other values (17) 17
63.0%

Length

2023-04-16T14:08:52.217163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ac 1
 
3.7%
al 1
 
3.7%
am 1
 
3.7%
ap 1
 
3.7%
ba 1
 
3.7%
ce 1
 
3.7%
df 1
 
3.7%
es 1
 
3.7%
go 1
 
3.7%
ma 1
 
3.7%
Other values (17) 17
63.0%

Most occurring characters

ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 54
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 54
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Estado
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
Acre
 
1
Paraíba
 
1
São Paulo
 
1
Sergipe
 
1
Santa Catarina
 
1
Other values (22)
22 

Length

Max length19
Median length16
Mean length9.4074074
Min length4

Characters and Unicode

Total characters254
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowAcre
2nd rowAlagoas
3rd rowAmazonas
4th rowAmapá
5th rowBahia

Common Values

ValueCountFrequency (%)
Acre 1
 
3.7%
Paraíba 1
 
3.7%
São Paulo 1
 
3.7%
Sergipe 1
 
3.7%
Santa Catarina 1
 
3.7%
Rio Grande do Sul 1
 
3.7%
Roraima 1
 
3.7%
Rondônia 1
 
3.7%
Rio Grande do Norte 1
 
3.7%
Rio de Janeiro 1
 
3.7%
Other values (17) 17
63.0%

Length

2023-04-16T14:08:52.307740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rio 3
 
6.8%
do 3
 
6.8%
sul 2
 
4.5%
grosso 2
 
4.5%
grande 2
 
4.5%
mato 2
 
4.5%
federal 1
 
2.3%
ceará 1
 
2.3%
bahia 1
 
2.3%
amapá 1
 
2.3%
Other values (26) 26
59.1%

Most occurring characters

ValueCountFrequency (%)
a 37
14.6%
o 27
 
10.6%
r 20
 
7.9%
i 17
 
6.7%
17
 
6.7%
n 15
 
5.9%
e 13
 
5.1%
s 12
 
4.7%
t 10
 
3.9%
d 8
 
3.1%
Other values (27) 78
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 197
77.6%
Uppercase Letter 40
 
15.7%
Space Separator 17
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 37
18.8%
o 27
13.7%
r 20
10.2%
i 17
8.6%
n 15
7.6%
e 13
 
6.6%
s 12
 
6.1%
t 10
 
5.1%
d 8
 
4.1%
u 5
 
2.5%
Other values (12) 33
16.8%
Uppercase Letter
ValueCountFrequency (%)
G 6
15.0%
S 6
15.0%
P 6
15.0%
R 5
12.5%
M 4
10.0%
A 4
10.0%
C 2
 
5.0%
J 1
 
2.5%
N 1
 
2.5%
E 1
 
2.5%
Other values (4) 4
10.0%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 237
93.3%
Common 17
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 37
15.6%
o 27
 
11.4%
r 20
 
8.4%
i 17
 
7.2%
n 15
 
6.3%
e 13
 
5.5%
s 12
 
5.1%
t 10
 
4.2%
d 8
 
3.4%
G 6
 
2.5%
Other values (26) 72
30.4%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243
95.7%
None 11
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 37
15.2%
o 27
11.1%
r 20
 
8.2%
i 17
 
7.0%
17
 
7.0%
n 15
 
6.2%
e 13
 
5.3%
s 12
 
4.9%
t 10
 
4.1%
d 8
 
3.3%
Other values (23) 67
27.6%
None
ValueCountFrequency (%)
á 5
45.5%
í 3
27.3%
ã 2
 
18.2%
ô 1
 
9.1%

Casos_entre_nascidos
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0080840224
Minimum0.0037340002
Maximum0.015481651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:52.392785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0037340002
5-th percentile0.0048103979
Q10.0069019435
median0.0078181381
Q30.0087117728
95-th percentile0.013268361
Maximum0.015481651
Range0.011747651
Interquartile range (IQR)0.0018098294

Descriptive statistics

Standard deviation0.0025271615
Coefficient of variation (CV)0.31261189
Kurtosis2.5132069
Mean0.0080840224
Median Absolute Deviation (MAD)0.0010940364
Skewness1.2587199
Sum0.2182686
Variance6.3865452 × 10-6
MonotonicityNot monotonic
2023-04-16T14:08:52.478360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.009094068582 1
 
3.7%
0.008237369367 1
 
3.7%
0.01389003163 1
 
3.7%
0.01181779721 1
 
3.7%
0.007787112028 1
 
3.7%
0.009760660326 1
 
3.7%
0.005858054825 1
 
3.7%
0.008329477094 1
 
3.7%
0.00814127999 1
 
3.7%
0.007088596113 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.003734000243 1
3.7%
0.004656021874 1
3.7%
0.005170608568 1
3.7%
0.005858054825 1
3.7%
0.006200040992 1
3.7%
0.006534603697 1
3.7%
0.006724101702 1
3.7%
0.007079785247 1
3.7%
0.007088596113 1
3.7%
0.007098644921 1
3.7%
ValueCountFrequency (%)
0.01548165138 1
3.7%
0.01389003163 1
3.7%
0.01181779721 1
3.7%
0.009760660326 1
3.7%
0.009586437586 1
3.7%
0.009363066944 1
3.7%
0.009094068582 1
3.7%
0.008329477094 1
3.7%
0.008237369367 1
3.7%
0.00814127999 1
3.7%

Espinha bífidaprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024878515
Minimum0.0057418466
Maximum0.045127627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:52.565180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0057418466
5-th percentile0.0071560888
Q10.017716898
median0.026757976
Q30.031514746
95-th percentile0.042928705
Maximum0.045127627
Range0.03938578
Interquartile range (IQR)0.013797848

Descriptive statistics

Standard deviation0.010775586
Coefficient of variation (CV)0.43312817
Kurtosis-0.55438112
Mean0.024878515
Median Absolute Deviation (MAD)0.0082290285
Skewness0.00027599203
Sum0.67171991
Variance0.00011611325
MonotonicityNot monotonic
2023-04-16T14:08:52.642620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.01737720111 1
 
3.7%
0.02995407043 1
 
3.7%
0.03307542184 1
 
3.7%
0.04512762657 1
 
3.7%
0.02716434428 1
 
3.7%
0.03498700483 1
 
3.7%
0.007510326699 1
 
3.7%
0.03559605596 1
 
3.7%
0.028782303 1
 
3.7%
0.02675797637 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.005741846578 1
3.7%
0.007004272606 1
3.7%
0.007510326699 1
3.7%
0.01202790474 1
3.7%
0.01327459734 1
3.7%
0.0173184164 1
3.7%
0.01737720111 1
3.7%
0.01805659453 1
3.7%
0.01878800301 1
3.7%
0.01910418729 1
3.7%
ValueCountFrequency (%)
0.04512762657 1
3.7%
0.04437580986 1
3.7%
0.03955212611 1
3.7%
0.03573057625 1
3.7%
0.03559605596 1
3.7%
0.03498700483 1
3.7%
0.03307542184 1
3.7%
0.02995407043 1
3.7%
0.028782303 1
3.7%
0.02866972477 1
3.7%

Outras malformações congênitas do sistema nervosoprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.071189688
Minimum0.038523499
Maximum0.11537711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:52.742789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.038523499
5-th percentile0.049155214
Q10.056785997
median0.068320011
Q30.082721137
95-th percentile0.099680794
Maximum0.11537711
Range0.076853606
Interquartile range (IQR)0.02593514

Descriptive statistics

Standard deviation0.018856581
Coefficient of variation (CV)0.26487799
Kurtosis-0.29968004
Mean0.071189688
Median Absolute Deviation (MAD)0.012499266
Skewness0.55869397
Sum1.9221216
Variance0.00035557065
MonotonicityNot monotonic
2023-04-16T14:08:52.824300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0984708063 1
 
3.7%
0.07821340611 1
 
3.7%
0.09971992852 1
 
3.7%
0.09025525314 1
 
3.7%
0.06539564364 1
 
3.7%
0.05426555851 1
 
3.7%
0.06759294029 1
 
3.7%
0.06407290072 1
 
3.7%
0.05756460599 1
 
3.7%
0.05487652781 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.03852349933 1
3.7%
0.04811161896 1
3.7%
0.05159027008 1
3.7%
0.05255100875 1
3.7%
0.05426555851 1
3.7%
0.05487652781 1
3.7%
0.05625943201 1
3.7%
0.05731256187 1
3.7%
0.05756460599 1
3.7%
0.05768787016 1
3.7%
ValueCountFrequency (%)
0.1153771056 1
3.7%
0.09971992852 1
3.7%
0.09958947849 1
3.7%
0.0984708063 1
3.7%
0.09174311927 1
3.7%
0.09025525314 1
3.7%
0.08462299738 1
3.7%
0.08081927655 1
3.7%
0.07821340611 1
3.7%
0.0761712124 1
3.7%

Malformações congênitas do aparelho circulatórioprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06098039
Minimum0.0069469772
Maximum0.29636236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:52.920943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0069469772
5-th percentile0.0083526449
Q10.029721628
median0.045127627
Q30.076024644
95-th percentile0.1407319
Maximum0.29636236
Range0.28941538
Interquartile range (IQR)0.046303016

Descriptive statistics

Standard deviation0.058433598
Coefficient of variation (CV)0.95823589
Kurtosis9.9027198
Mean0.06098039
Median Absolute Deviation (MAD)0.020251726
Skewness2.7762856
Sum1.6464705
Variance0.0034144854
MonotonicityNot monotonic
2023-04-16T14:08:52.992598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.05213160334 1
 
3.7%
0.04493110564 1
 
3.7%
0.2963623618 1
 
3.7%
0.04512762657 1
 
3.7%
0.09356607475 1
 
3.7%
0.156370491 1
 
3.7%
0.007510326699 1
 
3.7%
0.02135763357 1
 
3.7%
0.03289406057 1
 
3.7%
0.05623710288 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.006946977197 1
3.7%
0.007510326699 1
3.7%
0.01031805402 1
3.7%
0.01720183486 1
3.7%
0.02008003328 1
3.7%
0.02135763357 1
3.7%
0.02654919467 1
3.7%
0.03289406057 1
3.7%
0.03586800574 1
3.7%
0.03655722348 1
3.7%
ValueCountFrequency (%)
0.2963623618 1
3.7%
0.156370491 1
3.7%
0.1042418411 1
3.7%
0.09409385061 1
3.7%
0.0940767169 1
3.7%
0.09356607475 1
3.7%
0.0866699358 1
3.7%
0.06537935229 1
3.7%
0.06303845346 1
3.7%
0.05623710288 1
3.7%

Fenda labial e fenda palatinaprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062170109
Minimum0.03212977
Maximum0.11034777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:53.071377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.03212977
5-th percentile0.042795014
Q10.050156492
median0.056130222
Q30.075860073
95-th percentile0.092782559
Maximum0.11034777
Range0.078218004
Interquartile range (IQR)0.02570358

Descriptive statistics

Standard deviation0.017803439
Coefficient of variation (CV)0.28636654
Kurtosis0.77546486
Mean0.062170109
Median Absolute Deviation (MAD)0.0088450101
Skewness0.92660909
Sum1.678593
Variance0.00031696244
MonotonicityNot monotonic
2023-04-16T14:08:53.161258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.04633920297 1
 
3.7%
0.05325168076 1
 
3.7%
0.07997340802 1
 
3.7%
0.09589620646 1
 
3.7%
0.08551738015 1
 
3.7%
0.07782823523 1
 
3.7%
0.06008261359 1
 
3.7%
0.1103477735 1
 
3.7%
0.04728521206 1
 
3.7%
0.04399192725 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.03212976953 1
3.7%
0.04228205078 1
3.7%
0.04399192725 1
3.7%
0.04633920297 1
3.7%
0.04728521206 1
3.7%
0.04789654347 1
3.7%
0.04953200793 1
3.7%
0.0507809764 1
3.7%
0.05288002683 1
3.7%
0.05325168076 1
3.7%
ValueCountFrequency (%)
0.1103477735 1
3.7%
0.09589620646 1
3.7%
0.08551738015 1
3.7%
0.07997340802 1
3.7%
0.07910425221 1
3.7%
0.07873158929 1
3.7%
0.07782823523 1
3.7%
0.07389190999 1
3.7%
0.06537958619 1
3.7%
0.0630733945 1
3.7%

Ausência atresia e estenose do intestino delgadoprop
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0020755003
Minimum0
Maximum0.0080186032
Zeros8
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:53.242506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0018141001
Q30.0029371713
95-th percentile0.0062075188
Maximum0.0080186032
Range0.0080186032
Interquartile range (IQR)0.0029371713

Descriptive statistics

Standard deviation0.0020673113
Coefficient of variation (CV)0.99605443
Kurtosis2.1126502
Mean0.0020755003
Median Absolute Deviation (MAD)0.0013224483
Skewness1.3259667
Sum0.056038508
Variance4.2737759 × 10-6
MonotonicityNot monotonic
2023-04-16T14:08:53.334654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 8
29.6%
0.0004916517532 1
 
3.7%
0.007075836512 1
 
3.7%
0.003018260476 1
 
3.7%
0.002856082027 1
 
3.7%
0.004111757571 1
 
3.7%
0.001814100093 1
 
3.7%
0.002560376887 1
 
3.7%
0.001913948859 1
 
3.7%
0.002157388697 1
 
3.7%
Other values (10) 10
37.0%
ValueCountFrequency (%)
0 8
29.6%
0.0004916517532 1
 
3.7%
0.001664115024 1
 
3.7%
0.001708000273 1
 
3.7%
0.001751068152 1
 
3.7%
0.001775032394 1
 
3.7%
0.001814100093 1
 
3.7%
0.001913948859 1
 
3.7%
0.002151432137 1
 
3.7%
0.002157388697 1
 
3.7%
ValueCountFrequency (%)
0.008018603159 1
3.7%
0.007075836512 1
3.7%
0.004181444271 1
3.7%
0.004111757571 1
3.7%
0.003463683281 1
3.7%
0.003040900106 1
3.7%
0.003018260476 1
3.7%
0.002856082027 1
3.7%
0.002560376887 1
3.7%
0.002284826467 1
3.7%

Outras malformações congênitas do aparelho digestivoprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.079947979
Minimum0.02253098
Maximum0.94610092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:53.420909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.02253098
5-th percentile0.025804696
Q10.038122498
median0.044102317
Q30.052891508
95-th percentile0.10582269
Maximum0.94610092
Range0.92356994
Interquartile range (IQR)0.01476901

Descriptive statistics

Standard deviation0.17413639
Coefficient of variation (CV)2.1781212
Kurtosis26.287036
Mean0.079947979
Median Absolute Deviation (MAD)0.0084297272
Skewness5.1002023
Sum2.1585954
Variance0.030323483
MonotonicityNot monotonic
2023-04-16T14:08:53.492599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.02896200185 1
 
3.7%
0.0632363709 1
 
3.7%
0.1240739705 1
 
3.7%
0.05923000987 1
 
3.7%
0.05533477539 1
 
3.7%
0.05069545597 1
 
3.7%
0.0225309801 1
 
3.7%
0.04983447834 1
 
3.7%
0.05139696963 1
 
3.7%
0.03220027665 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.0225309801 1
3.7%
0.02458258766 1
3.7%
0.02865628094 1
3.7%
0.02896200185 1
3.7%
0.031554338 1
3.7%
0.03220027665 1
3.7%
0.0374029458 1
3.7%
0.03884204995 1
3.7%
0.03953365492 1
3.7%
0.04099200656 1
3.7%
ValueCountFrequency (%)
0.9461009174 1
3.7%
0.1240739705 1
3.7%
0.0632363709 1
3.7%
0.06316031236 1
3.7%
0.05923000987 1
3.7%
0.05533477539 1
3.7%
0.05325097183 1
3.7%
0.05253204455 1
3.7%
0.05139696963 1
3.7%
0.05069545597 1
3.7%

Testiculo não-descidoprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016268508
Minimum0
Maximum0.042572601
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:53.580194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0031601729
Q10.0079342033
median0.014200259
Q30.020469012
95-th percentile0.041265452
Maximum0.042572601
Range0.042572601
Interquartile range (IQR)0.012534809

Descriptive statistics

Standard deviation0.011975625
Coefficient of variation (CV)0.73612313
Kurtosis0.21725756
Mean0.016268508
Median Absolute Deviation (MAD)0.0066899325
Skewness1.0051811
Sum0.43924972
Variance0.0001434156
MonotonicityNot monotonic
2023-04-16T14:08:53.660270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.01737720111 1
 
3.7%
0.0349464155 1
 
3.7%
0.03883482365 1
 
3.7%
0.04230714991 1
 
3.7%
0.02313999698 1
 
3.7%
0.01713649216 1
 
3.7%
0.007510326699 1
 
3.7%
0.01779802798 1
 
3.7%
0.0143911515 1
 
3.7%
0.01496632577 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0 1
3.7%
0.002868576182 1
3.7%
0.003840565331 1
3.7%
0.003869270256 1
3.7%
0.005733944954 1
3.7%
0.006854479402 1
3.7%
0.007510326699 1
3.7%
0.008358079805 1
3.7%
0.008743019839 1
3.7%
0.01050640891 1
3.7%
ValueCountFrequency (%)
0.04257260149 1
3.7%
0.04230714991 1
3.7%
0.03883482365 1
3.7%
0.0349464155 1
3.7%
0.02655490515 1
3.7%
0.02588866436 1
3.7%
0.02313999698 1
3.7%
0.01779802798 1
3.7%
0.01737720111 1
3.7%
0.01713649216 1
3.7%

Outras malformações do aparelho geniturinárioprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.079500787
Minimum0.030041307
Maximum0.1692286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:53.735334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.030041307
5-th percentile0.03528457
Q10.057734103
median0.075301205
Q30.096479269
95-th percentile0.14044792
Maximum0.1692286
Range0.13918729
Interquartile range (IQR)0.038745166

Descriptive statistics

Standard deviation0.034078188
Coefficient of variation (CV)0.42865222
Kurtosis0.640207
Mean0.079500787
Median Absolute Deviation (MAD)0.02046537
Skewness0.80894871
Sum2.1465212
Variance0.0011613229
MonotonicityNot monotonic
2023-04-16T14:08:53.819430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.07530120482 1
 
3.7%
0.1065033615 1
 
3.7%
0.1359218828 1
 
3.7%
0.1692285996 1
 
3.7%
0.0875295538 1
 
3.7%
0.09710678891 1
 
3.7%
0.0300413068 1
 
3.7%
0.07119211191 1
 
3.7%
0.0986821817 1
 
3.7%
0.07664572893 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.0300413068 1
3.7%
0.03440366972 1
3.7%
0.03734000243 1
3.7%
0.0374029458 1
3.7%
0.04374578678 1
3.7%
0.05483583522 1
3.7%
0.0556881973 1
3.7%
0.05978000956 1
3.7%
0.06293142441 1
3.7%
0.06303845346 1
3.7%
ValueCountFrequency (%)
0.1692285996 1
3.7%
0.142387654 1
3.7%
0.1359218828 1
3.7%
0.113273529 1
3.7%
0.1065033615 1
3.7%
0.0986821817 1
3.7%
0.09710678891 1
3.7%
0.09585174929 1
3.7%
0.09221393633 1
3.7%
0.08845473425 1
3.7%

Deformidades congênitas do quadrilprop
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0051127154
Minimum0
Maximum0.033898194
Zeros3
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:53.892660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0016805941
median0.0034160005
Q30.0061020787
95-th percentile0.014460779
Maximum0.033898194
Range0.033898194
Interquartile range (IQR)0.0044214846

Descriptive statistics

Standard deviation0.0067580835
Coefficient of variation (CV)1.3218188
Kurtosis13.176507
Mean0.0051127154
Median Absolute Deviation (MAD)0.0020554255
Skewness3.3622224
Sum0.13804332
Variance4.5671692 × 10-5
MonotonicityNot monotonic
2023-04-16T14:08:54.090368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 3
 
11.1%
0.01737720111 1
 
3.7%
0.001438259131 1
 
3.7%
0.03389819352 1
 
3.7%
0.002820476661 1
 
3.7%
0.006036520952 1
 
3.7%
0.00428412304 1
 
3.7%
0.007510326699 1
 
3.7%
0.007119211191 1
 
3.7%
0.006167636356 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
0 3
11.1%
0.0007171440456 1
 
3.7%
0.001280188444 1
 
3.7%
0.00136057507 1
 
3.7%
0.001438259131 1
 
3.7%
0.001922929005 1
 
3.7%
0.002284826467 1
 
3.7%
0.002579513504 1
 
3.7%
0.002660919081 1
 
3.7%
0.002820476661 1
 
3.7%
ValueCountFrequency (%)
0.03389819352 1
3.7%
0.01737720111 1
3.7%
0.007655795437 1
3.7%
0.007510326699 1
3.7%
0.007119211191 1
3.7%
0.006927366562 1
3.7%
0.006167636356 1
3.7%
0.006036520952 1
3.7%
0.005321575186 1
3.7%
0.004424865779 1
3.7%

Deformidades congênitas dos pésprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11571701
Minimum0.060082614
Maximum0.19461289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:54.177212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.060082614
5-th percentile0.065156207
Q10.093291694
median0.10871755
Q30.13278628
95-th percentile0.18467831
Maximum0.19461289
Range0.13453028
Interquartile range (IQR)0.039494585

Descriptive statistics

Standard deviation0.035329844
Coefficient of variation (CV)0.30531245
Kurtosis0.069121003
Mean0.11571701
Median Absolute Deviation (MAD)0.022925919
Skewness0.62466774
Sum3.1243592
Variance0.0012481978
MonotonicityNot monotonic
2023-04-16T14:08:54.266169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.1274328082 1
 
3.7%
0.1497703521 1
 
3.7%
0.13164347 1
 
3.7%
0.1946128896 1
 
3.7%
0.1016147694 1
 
3.7%
0.1235255477 1
 
3.7%
0.06008261359 1
 
3.7%
0.1103477735 1
 
3.7%
0.1685820604 1
 
3.7%
0.08571622939 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.06008261359 1
3.7%
0.0621261338 1
3.7%
0.07222637811 1
3.7%
0.07815349346 1
3.7%
0.08405127128 1
3.7%
0.08571622939 1
3.7%
0.09036014974 1
3.7%
0.09622323791 1
3.7%
0.09906401585 1
3.7%
0.1016147694 1
3.7%
ValueCountFrequency (%)
0.1946128896 1
3.7%
0.1915767067 1
3.7%
0.1685820604 1
3.7%
0.1588577553 1
3.7%
0.1497703521 1
3.7%
0.1365279594 1
3.7%
0.1339290869 1
3.7%
0.13164347 1
3.7%
0.1316007105 1
3.7%
0.1274328082 1
3.7%

Outras malformações e deformidades congênitas do aparelho osteomuscularprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26025708
Minimum0.11375675
Maximum0.39486673
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:54.366526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.11375675
5-th percentile0.14814878
Q10.21981497
median0.26791343
Q30.29806268
95-th percentile0.35632863
Maximum0.39486673
Range0.28110998
Interquartile range (IQR)0.078247708

Descriptive statistics

Standard deviation0.066042093
Coefficient of variation (CV)0.25375715
Kurtosis0.040949338
Mean0.26025708
Median Absolute Deviation (MAD)0.03733339
Skewness-0.20879454
Sum7.0269411
Variance0.0043615581
MonotonicityNot monotonic
2023-04-16T14:08:54.442481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.2838276182 1
 
3.7%
0.2013579179 1
 
3.7%
0.3595512274 1
 
3.7%
0.3948667325 1
 
3.7%
0.2102721465 1
 
3.7%
0.2620455259 1
 
3.7%
0.1952684942 1
 
3.7%
0.3381625316 1
 
3.7%
0.2981024239 1
 
3.7%
0.2807319894 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.1137567516 1
3.7%
0.1418732427 1
3.7%
0.1627916983 1
3.7%
0.1952684942 1
3.7%
0.2013579179 1
3.7%
0.2086707163 1
3.7%
0.2102721465 1
3.7%
0.2293577982 1
3.7%
0.2305800369 1
3.7%
0.2354157097 1
3.7%
ValueCountFrequency (%)
0.3948667325 1
3.7%
0.3595512274 1
3.7%
0.3488092374 1
3.7%
0.3381625316 1
3.7%
0.3250465636 1
3.7%
0.3043324352 1
3.7%
0.2981024239 1
3.7%
0.2980229371 1
3.7%
0.2845934928 1
3.7%
0.2838276182 1
3.7%

Outras malformações congênitasprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13184662
Minimum0.04862884
Maximum0.24538147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:54.538427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.04862884
5-th percentile0.07206874
Q10.10342002
median0.11670607
Q30.15813495
95-th percentile0.22509814
Maximum0.24538147
Range0.19675263
Interquartile range (IQR)0.054714931

Descriptive statistics

Standard deviation0.048403458
Coefficient of variation (CV)0.36711944
Kurtosis0.25290467
Mean0.13184662
Median Absolute Deviation (MAD)0.019610182
Skewness0.81237038
Sum3.5598588
Variance0.0023428947
MonotonicityNot monotonic
2023-04-16T14:08:54.629289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.2143188137 1
 
3.7%
0.1863808826 1
 
3.7%
0.2297178551 1
 
3.7%
0.2453814695 1
 
3.7%
0.1167060717 1
 
3.7%
0.163510696 1
 
3.7%
0.1051445738 1
 
3.7%
0.1566226462 1
 
3.7%
0.1192409696 1
 
3.7%
0.1129277308 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.04862884038 1
3.7%
0.06669439624 1
3.7%
0.08460887624 1
3.7%
0.08641370238 1
3.7%
0.09747706422 1
3.7%
0.09959439991 1
3.7%
0.1017749813 1
3.7%
0.1050650595 1
3.7%
0.1051445738 1
3.7%
0.1058960169 1
3.7%
ValueCountFrequency (%)
0.2453814695 1
3.7%
0.2297178551 1
3.7%
0.2143188137 1
3.7%
0.1952227836 1
3.7%
0.1863808826 1
3.7%
0.163510696 1
3.7%
0.1596472556 1
3.7%
0.1566226462 1
3.7%
0.1438259131 1
3.7%
0.1363162537 1
3.7%

Anomalias cromossômicas não classificadas em outra parteprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.049610053
Minimum0.0078153493
Maximum0.10295188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:54.720064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0078153493
5-th percentile0.017478633
Q10.033062511
median0.046373727
Q30.061793345
95-th percentile0.093777478
Maximum0.10295188
Range0.09513653
Interquartile range (IQR)0.028730834

Descriptive statistics

Standard deviation0.024092645
Coefficient of variation (CV)0.48564037
Kurtosis-0.12007462
Mean0.049610053
Median Absolute Deviation (MAD)0.01633242
Skewness0.46651943
Sum1.3394714
Variance0.00058045552
MonotonicityNot monotonic
2023-04-16T14:08:54.804059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0984708063 1
 
3.7%
0.05325168076 1
 
3.7%
0.07783420163 1
 
3.7%
0.05923000987 1
 
3.7%
0.06941999095 1
 
3.7%
0.08282637877 1
 
3.7%
0.0300413068 1
 
3.7%
0.06051329513 1
 
3.7%
0.05550872721 1
 
3.7%
0.04217782716 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.007815349346 1
3.7%
0.01676683777 1
3.7%
0.01913948859 1
3.7%
0.02284826467 1
3.7%
0.02510004159 1
3.7%
0.02605754292 1
3.7%
0.0300413068 1
3.7%
0.03608371422 1
3.7%
0.03677243118 1
3.7%
0.0417095148 1
3.7%
ValueCountFrequency (%)
0.1029518789 1
3.7%
0.0984708063 1
3.7%
0.08282637877 1
3.7%
0.07783420163 1
3.7%
0.07361083551 1
3.7%
0.06941999095 1
3.7%
0.0630733945 1
3.7%
0.06051329513 1
3.7%
0.05923000987 1
3.7%
0.05550872721 1
3.7%

Interactions

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2023-04-16T14:08:42.625094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:43.818482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:45.087305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:46.501805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:47.783674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:49.039641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:50.451213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-16T14:08:54.892372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Casos_entre_nascidosEspinha bífidapropOutras malformações congênitas do sistema nervosopropMalformações congênitas do aparelho circulatóriopropFenda labial e fenda palatinapropAusência atresia e estenose do intestino delgadopropOutras malformações congênitas do aparelho digestivopropTesticulo não-descidopropOutras malformações do aparelho geniturináriopropDeformidades congênitas do quadrilpropDeformidades congênitas dos péspropOutras malformações e deformidades congênitas do aparelho osteomuscularpropOutras malformações congênitaspropAnomalias cromossômicas não classificadas em outra partepropUFEstado
Casos_entre_nascidos1.0000.6180.5130.4270.5440.1870.7000.7260.7000.2950.6760.5050.6790.6941.0001.000
Espinha bífidaprop0.6181.0000.3600.2590.4710.1170.4710.5130.613-0.2300.3940.2970.3750.5551.0001.000
Outras malformações congênitas do sistema nervosoprop0.5130.3601.0000.0990.308-0.0600.2890.3650.2750.0690.3330.0020.2920.4801.0001.000
Malformações congênitas do aparelho circulatórioprop0.4270.2590.0991.0000.3260.6610.3940.4730.6090.1550.1970.3640.4710.5631.0001.000
Fenda labial e fenda palatinaprop0.5440.4710.3080.3261.0000.1270.5420.4620.3880.1330.3030.2250.3170.4621.0001.000
Ausência atresia e estenose do intestino delgadoprop0.1870.117-0.0600.6610.1271.0000.2300.2290.4070.2090.2390.2660.1840.1641.0001.000
Outras malformações congênitas do aparelho digestivoprop0.7000.4710.2890.3940.5420.2301.0000.4830.5290.1220.4520.1820.5200.5071.0001.000
Testiculo não-descidoprop0.7260.5130.3650.4730.4620.2290.4831.0000.8790.4740.6520.5240.7080.5051.0001.000
Outras malformações do aparelho geniturinárioprop0.7000.6130.2750.6090.3880.4070.5290.8791.0000.2500.6390.6000.6900.4781.0001.000
Deformidades congênitas do quadrilprop0.295-0.2300.0690.1550.1330.2090.1220.4740.2501.0000.3710.3290.5520.1671.0001.000
Deformidades congênitas dos pésprop0.6760.3940.3330.1970.3030.2390.4520.6520.6390.3711.0000.4620.5510.2831.0001.000
Outras malformações e deformidades congênitas do aparelho osteomuscularprop0.5050.2970.0020.3640.2250.2660.1820.5240.6000.3290.4621.0000.5080.2521.0001.000
Outras malformações congênitasprop0.6790.3750.2920.4710.3170.1840.5200.7080.6900.5520.5510.5081.0000.4131.0001.000
Anomalias cromossômicas não classificadas em outra parteprop0.6940.5550.4800.5630.4620.1640.5070.5050.4780.1670.2830.2520.4131.0001.0001.000
UF1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Estado1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-04-16T14:08:51.793493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-16T14:08:52.027082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

UFEstadoCasos_entre_nascidosEspinha bífidapropOutras malformações congênitas do sistema nervosopropMalformações congênitas do aparelho circulatóriopropFenda labial e fenda palatinapropAusência atresia e estenose do intestino delgadopropOutras malformações congênitas do aparelho digestivopropTesticulo não-descidopropOutras malformações do aparelho geniturináriopropDeformidades congênitas do quadrilpropDeformidades congênitas dos péspropOutras malformações e deformidades congênitas do aparelho osteomuscularpropOutras malformações congênitaspropAnomalias cromossômicas não classificadas em outra parteprop
0ACAcre0.0090940.0173770.0984710.0521320.0463390.0000000.0289620.0173770.0753010.0173770.1274330.2838280.2143190.098471
1ALAlagoas0.0076340.0249980.0576880.0384590.0653800.0000000.0423040.0153830.0884550.0019230.1365280.2845930.0846090.044227
2AMAmazonas0.0046560.0180570.0515900.0103180.0528800.0000000.0374030.0038690.0374030.0025800.0722260.1418730.0864140.016767
3APAmapá0.0154820.0286700.0917430.0172020.0630730.0000000.9461010.0057340.0344040.0000000.1032110.2293580.0974770.063073
4BABahia0.0070800.0132750.0693230.0265490.0422820.0004920.0245830.0083580.0629310.0044250.1022640.3043320.1155380.026058
5CECeará0.0095860.0357310.0995890.0653790.0592980.0030410.0478940.0425730.1132740.0053220.1915770.2523950.1596470.046374
6DFDistrito Federal0.0067240.0070040.0385230.0630380.0507810.0017510.0525320.0105060.0630380.0035020.0840510.2679130.1155700.036772
7ESEspírito Santo0.0079520.0443760.1153770.0940770.0621260.0017750.0532510.0142000.0958520.0000000.0621260.2502800.1242520.102952
8GOGoiás0.0080470.0173180.0808190.0473370.0738920.0034640.0427190.0265550.0842830.0069270.1339290.2701670.1050650.050801
9MAMaranhão0.0037340.0191040.0573130.0069470.0321300.0000000.0286560.0000000.0373400.0000000.0781530.1137570.0486290.007815
UFEstadoCasos_entre_nascidosEspinha bífidapropOutras malformações congênitas do sistema nervosopropMalformações congênitas do aparelho circulatóriopropFenda labial e fenda palatinapropAusência atresia e estenose do intestino delgadopropOutras malformações congênitas do aparelho digestivopropTesticulo não-descidopropOutras malformações do aparelho geniturináriopropDeformidades congênitas do quadrilpropDeformidades congênitas dos péspropOutras malformações e deformidades congênitas do aparelho osteomuscularpropOutras malformações congênitaspropAnomalias cromossômicas não classificadas em outra parteprop
17PRParaná0.0070990.0236830.0761710.0940940.0787320.0025600.0428860.0038410.0556880.0012800.1056160.2086710.1017750.073611
18RJRio de Janeiro0.0070890.0267580.0548770.0562370.0439920.0018140.0322000.0149660.0766460.0013610.0857160.2807320.1129280.042178
19RNRio Grande do Norte0.0081410.0287820.0575650.0328940.0472850.0041120.0513970.0143910.0986820.0061680.1685820.2981020.1192410.055509
20RORondônia0.0083290.0355960.0640730.0213580.1103480.0000000.0498340.0177980.0711920.0071190.1103480.3381630.1566230.060513
21RRRoraima0.0058580.0075100.0675930.0075100.0600830.0000000.0225310.0075100.0300410.0075100.0600830.1952680.1051450.030041
22RSRio Grande do Sul0.0097610.0349870.0542660.1563700.0778280.0028560.0506950.0171360.0971070.0042840.1235260.2620460.1635110.082826
23SCSanta Catarina0.0077870.0271640.0653960.0935660.0855170.0030180.0553350.0231400.0875300.0060370.1016150.2102720.1167060.069420
24SESergipe0.0118180.0451280.0902550.0451280.0958960.0000000.0592300.0423070.1692290.0028200.1946130.3948670.2453810.059230
25SPSão Paulo0.0138900.0330750.0997200.2963620.0799730.0070760.1240740.0388350.1359220.0338980.1316430.3595510.2297180.077834
26TOTocantins0.0078180.0120280.0481120.1042420.0561300.0080190.0441020.0120280.0922140.0040090.0962230.3488090.1363160.036084